The ONNX Runtime extensions library was not found. The Microsoft.ML.OnnxRuntime.Extensions NuGet package must be referenced by the project to use OrtExtensions.RegisterCustomOps

Introduction

------------

ONNX (Open Neural Network Exchange) is an open-source format for representing machine learning models. It allows developers to use different deep learning frameworks such as TensorFlow, PyTorch, and Caffe to train models and then convert them into ONNX format. This makes it easier to share models across different platforms and tools.

The ONNX Runtime is a set of libraries that provides an execution engine for running ONNX models. It includes a number of built-in operations that can be used to build custom models. However, sometimes developers need to add their own custom operations to the ONNX Runtime. This is where the OrtExtensions library comes in.

OrtExtensions is a set of extensions for the ONNX Runtime that allows developers to register custom operations. These extensions provide a simple and consistent way to add new operations to the ONNX Runtime without having to modify the core codebase. In this blog post, we will explore how to use OrtExtensions to register custom operations in the ONNX Runtime.

Prerequisites

------------

Before we begin, you should have a basic understanding of the following concepts:

* C# programming language

* .NET Framework or .NET Core

* ONNX (Open Neural Network Exchange) format

* Deep learning frameworks such as TensorFlow, PyTorch, and Caffe

You should also have the Microsoft.ML.OnnxRuntime.Extensions NuGet package installed in your project. You can install it using the following command:

bash
dotnet add package Microsoft.ML.OnnxRuntime.Extensions

Installing OrtExtensions

-----------------------

OrtExtensions is not a separate NuGet package, but rather an extension to the ONNX Runtime. To use OrtExtensions, you need to include it in your project.

Assuming you have already installed the Microsoft.ML.OnnxRuntime.Extensions NuGet package, you can include OrtExtensions by adding the following line to your project file:

csharp
  

This will add a reference to the Microsoft.ML.OnnxRuntime.Extensions assembly, which includes the OrtExtensions extension.

Registering Custom Operations

---------------------------

To register a custom operation in the ONNX Runtime using OrtExtensions, you need to create a new class that inherits from the `ONNXOperation` class. This class provides a base implementation of an ONNX operation that can be executed by the ONNX Runtime.

Here is an example of how to create a custom operation:

csharp
using Microsoft.ML;
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
namespace MyCustomOperation
{
    public class MyCustomOperation : ONNXOperation
    {
        // Define input and output ports
        [Input(0)]
        public Tensor Input1 { get; set; }
        [Input(1)]
        public Tensor Input2 { get; set; }
        [Output(0)]
        public Tensor Output { get; set; }
        // Define the operation logic
        protected override void Infer(MLContext context, MLModel model)
        {
            // Get input tensors
            var input1 = Input1.AsTensor();
            var input2 = Input2.AsTensor();
            // Perform some custom operation on the inputs
            var result = input1 + input2;
            // Set the output tensor
            Output.Set(result);
        }
    }
}

In this example, we define a custom operation called `MyCustomOperation` that takes two input tensors (`Input1` and `Input2`) and produces one output tensor (`Output`). The `Infer` method is where you can define the logic of your custom operation. In this case, we simply add the two input tensors together to produce the output.

Registering the Custom Operation with the ONNX Runtime

------------------------------------------------------

Once you have defined your custom operation, you need to register it with the ONNX Runtime using OrtExtensions. To do this, you can use the `OrtExtensions.RegisterCustomOps` method, which takes a list of custom operations as its argument.

Here is an example of how to register the `MyCustomOperation` custom operation:

csharp
using Microsoft.ML;
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
namespace MyCustomOperation
{
    class Program
    {
        static void Main(string[] args)
        {
            // Create a new ONNX model
            var model = new MLModel();
            // Register the custom operation with the ONNX Runtime
            OrtExtensions.RegisterCustomOps(model, new MyCustomOperation());
            // Save the model to disk
            model.SaveToFile("mycustomoperation.onnx");
        }
    }
}

In this example, we create a new `MLModel` object and use the `OrtExtensions.RegisterCustomOps` method to register our custom operation (`MyCustomOperation`) with the ONNX Runtime. We then save the model to disk using the `SaveToFile` method.

Using the Custom Operation in an ONNX Model

-----------------------------------------------

Once you have registered your custom operation with the ONNX Runtime, you can use it in an ONNX model by adding it as an input or output port.

Here is an example of how to use the `MyCustomOperation` custom operation in an ONNX model:

csharp
using Microsoft.ML;
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
namespace MyCustomOperation
{
    class Program
    {
        static void Main(string[] args)
        {
            // Create a new ONNX model
            var model = new MLModel();
            // Add input ports to the model
            model.AddInputPort("input1", "Input1");
            model.AddInputPort("input2", "Input2");
            // Add output ports to the model
            model.AddOutputPort("output", "Output");
            // Add the custom operation as an input port
            model.AddInputPort>("custom_operation_input1", "CustomOperationInput1");
            model.AddInputPort>("custom_operation_input2", "CustomOperationInput2");
            // Add the custom operation as an output port
            model.AddOutputPort>("custom_operation_output", "CustomOperationOutput");
            // Register the custom operation with the ONNX Runtime
            OrtExtensions.RegisterCustomOps(model, new MyCustomOperation());
            // Save the model to disk
            model.SaveToFile("mycustomoperation.onnx");
        }
    }
}

In this example, we create a new `MLModel` object and add two input ports (`input1` and `input2`) and one output port (`output`). We also add two additional input ports (`CustomOperationInput1` and `CustomOperationInput2`) and one output port (`CustomOperationOutput`) to represent the custom operation.

We then register our custom operation with the ONNX Runtime using OrtExtensions, just as we did in the previous example. Finally, we save the model to disk using the `SaveToFile` method.

Conclusion

----------

In this blog post, we explored how to use OrtExtensions to register custom operations in the ONNX Runtime. We discussed the prerequisites for using OrtExtensions, and provided an example of how to create a custom operation and register it with the ONNX Runtime. We also showed how to use the custom operation in an ONNX model by adding it as an input or output port.

By using OrtExtensions, developers can easily add their own custom operations to the ONNX Runtime without having to modify the core codebase. This makes it easier to build custom models that can be shared across different platforms and tools.